Senior Backend Engineer (Machine Learning Server Parameter) - EGO team

Shopee; Singapore; On-site; SGD 120,000 - 170,000; Full time

Job summary

Shopee’s EGO team is building an industry-leading machine learning platform to support algorithms across recommendation, search, and advertising. This role focuses on distributed Parameter Server systems for large-scale training and real-time inference, with TB-scale models and high-throughput needs.

Join a collaborative, high-performance environment that accelerates model iteration and delivers robust, end-to-end ML services within Shopee's platform.

Qualifications

• Bachelor's degree in CS or related field, with at least 3 years of work experience.
• Proficient in C++ with low-level optimization and debugging.
• Experience with distributed systems and high-throughput services.

Responsibilities

• Develop distributed Parameter Server (PS) systems for large-scale model training and inference.
• Integrate PS components into the one-stop ML platform to improve stability and performance.

Skills

C++ programming

multi-threaded programming

template programming

GDB debugging

performance tuning

RPC frameworks

distributed PS systems

team collaboration

Education

Bachelor's degree in Computer Science or related field

Tools

NVMe-SSD

Job description

About The Team

The EGO team is dedicated to building an industry-leading machine learning platform to effectively support the implementation of algorithms across various business domains such as recommendation, search, and advertising. It focuses on extreme optimization for CTR/CVR prediction in large-scale sparse parameter scenarios, ensuring maximum performance in e-commerce applications and delivering greater value to the company.

The EGO platform covers the entire deep machine learning workflow — from sample organization and training to model building and publishing, and further to online model loading and inference services. It comes with a user-friendly Web UI and Restful API, providing an end-to-end, one-stop machine learning platform.

Job Description

• Develop distributed Parameter Server (PS) systems for large-scale sparse model training and inference platforms in the search, advertising, and recommendation domains. The system should support high-throughput parameter read/write and update operations, handle hundreds of billions of features and TB‑level sparse models, enable online real‑time learning, and meet algorithmic needs such as feature admission and expiration.
• Participate in the development of the one‑stop machine learning platform, integrating the PS system into the platform to provide a user‑friendly, stable, high‑performance, and platform‑level distributed parameter service system. Enhance the platform’s efficiency and usability, accelerating the model iteration process for algorithm teams.

Requirements

• Bachelor’s degree or above in Computer Science, Electronics, Automation, Software Engineering, or related fields, with at least 3 years of work experience.
• Proficient in C++ programming with strong low‑level technical skills; adept at multi‑threaded programming, lock optimization, memory pool, thread pool, template programming, GDB debugging, performance tuning, and RPC frameworks.
• Familiarity with distributed PS systems, distributed system backend optimization, high‑performance in‑memory KV systems, KV storage systems based on NVMe‑SSD, and high‑performance client‑server architecture systems is a plus.
• Highly passionate about computer technology, proactive in learning, with a strong spirit of in-depth research and hands‑on practice. Maintains high standards and strict requirements for delivered code; works with rigor and attention to detail.
• Strong team player with excellent continuous learning ability.